Using LEL and scenarios to derive mathematical programming models. Application in a fresh tomato packing problem

Alejandra Garrido, Leandro Antonelli, Jonathan Martin, M.M.E. Alemany and Josefa Mula “Using LEL and scenarios to derive mathematical programming models. Application in a fresh tomato packing problem” Computers and Electronics in Agriculture Volume 170, March 2020, 105242

Mathematical programming models are invaluable tools at decision making, assisting managers to uncover otherwise unattainable means to optimize their processes. However, the value they provide is only as good as their capacity to capture the process domain. This information can only be obtained from stakeholders, i.e., clients or users, who can hardly communicate the requirements clearly and completely. Besides, existing conceptual models of mathematical programming models are not standardized, nor is the process of deriving the mathematical programming model from the concept model, which remains ad hoc. In this paper, we propose an agile methodology to construct mathematical programming models based on two techniques from requirements engineering that have been proven effective at requirements elicitation: the language extended lexicon (LEL) and scenarios. Using the pair of LEL + scenarios allows to create a conceptual model that is clear and complete enough to derive a mathematical programming model that effectively captures the business domain. We also define an ontology to describe the pair LEL + scenarios, which has been implemented with a semantic mediawiki and allows the collaborative construction of the conceptual model and the semi-automatic derivation of mathematical programming model elements. The process is applied and validated in a known fresh tomato packing optimization problem. This proposal can be of high relevance for the development and implementation of mathematical programming models for optimizing agriculture and supply chain management related processes in order to fill the current gap between mathematical programming models in the theory and the practice.

Link de descarga https://doi.org/10.1016/j.compag.2020.105242

Towards a collaborative experience to generate knowledge: Use of gamification in robotics for Good Agricultural Practices

Julieta Lombardelli, Diego Torres, Blas Butera and Alejandro Fernandez, “Towards a collaborative experience to generate knowledge: Use of gamification in robotics for Good Agricultural Practices,” EAI Endorsed Transactions on Creative Technologies: Online First, vol. , pp. , 3. 2020.

Currently, gamification strategies are implemented in the most diverse fields. This paper discusses the design of a gamification environment on a collaborative wiki on good agricultural practices. The aim of the project is to present a gamification design, complemented by a robotic interface, aimed at customizing the experience from a cooperative community context. The robot interface also presents the possibility of containing a living organism, linked to the actions of the wiki.

Link de descarga https://eudl.eu/doi/10.4108/eai.13-7-2018.163482

Convocatoria para beca doctoral en Francia

Se abre una convocatoria para aplicar a una beca doctoral a realizarse en el equipo CRAN, de la Universidad de Lorraine, en Nancy, Francia. El proyecto será dirigido en coordinación con investigadores de LIFIA (UNLP) y CRAN. El título del proyecto es: Métodos formales para la extracción y reutilización de conocimientos de fuentes heterogéneas (Cyber Physical System (CPS),  bases de datos distribuidas, cámaras de vigilancia, etc) para arquitecturas distribuidas. La fecha límite para la postulación es el 1 de marzo de 2020.

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